This paper investigates the critical role of church membership in the process of economic recovery after high-impact natural disasters. We document a significant adverse treatment effect of the 2005 hurricane season in the Southeastern United States on establishment-level productivity. However, we find that establishments in counties with higher rates of church membership saw a significantly stronger recovery in terms of productivity for 2005–10. We also show that church membership is correlated with post-disaster entrepreneurship activities and population growth.

Die IAB-Stellenerhebung ist eine quartalsweise durchgeführte und repräsentative Betriebsbefragung über das gesamtwirtschaftliche Stellenangebot sowie Einstellungsprozesse in Deutschland. Die Erhebung ermittelt die Gesamtzahl aller offenen Stellen am Arbeitsmarkt, einschließlich jener Stellen, die nicht der Bundesagentur für Arbeit (BA) gemeldet werden. Das erste Modul des Fragebogens enthält Informationen zur Zahl und Struktur offener Stellen, dem erwarteten künftigen Arbeitskräftebedarf, zur wirtschaftlichen Lage und zur Entwicklung der befragten Betriebe. Das zweite Modul erfragt die betriebliche Einschätzung und Nutzung aktueller arbeitsmarktpolitischer Instrumente sowie den betrieblichen Umgang mit am Arbeitsmarkt benachteiligten Personen. Das dritte Modul enthält Fragen zum letzten Fall einer Neueinstellung und zum letzten Fall eines gescheiterten Rekrutierungsversuchs. Das Forschungsdatenzentrum der Bundesagentur für Arbeit stellt die Datensätze der Befragungswellen ab 2000 mit allen Fragebogenteilen einschließlich der Quartalsbefragungen für externe Wissenschaftlerinnen und Wissenschaftler bereit.

We analyze how skill transferability and the local industry mix affect the adjustment costs of workers hit by a trade shock. Using German administrative data and novel measures of economic distance we construct an index of labor market absorptiveness that captures the degree to which workers from a particular industry are able to reallocate into other jobs. Among manufacturing workers, we find that the earnings loss associated with increased import exposure is much higher for those who live in the least absorptive regions. We conclude that the local industry composition plays an important role in the adjustment processes of workers.

Using the adoption of Zombie Property Law (ZL) across several US states, we show that increased lender accountability in the foreclosure process affects mortgage lending decisions and standards. Difference-in-differences estimations using a state border design show that ZL incentivizes lenders to screen mortgage applications more carefully: they deny more applications and impose higher interest rates on originated loans, especially risky loans. In turn, these loans exhibit higher ex-post performance. ZL also affects lender behavior after borrowers become distressed, causing them to strategically keep delinquent mortgages alive. Our findings inform the debate on policy responses to foreclosure crises.

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

The recent advances in automation technology, robotics in particular, have sparked a heated debate over the future of labor and human society at large. The ongoing process of robotization may engender profound impacts on various segments of the labor market. Given the far-reaching implications of robots, it is thus very important to understand the scale and scope of robot use and characteristics of robot users. However, the main challenge is the limited availability of robot data at the microeconomic level (Raj and Seamans, 2018). Due to the data constraint, the bulk of the existing literature relies on cross-country industry-level data from the International Federation of Robotics (IFR). The lack of micro-level robot data makes it difficult to paint a comprehensive picture of robotization in industrial settings, and perhaps more importantly, to assess how within-industry firm level heterogeneity manifests itself in robot use and adoption.

Why are children with lower socioeconomic status (SES) substantially less likely to be enrolled in child care? We study whether barriers in the application process work against lower-SES children — the group known to benefit strongest from child care enrollment. In an RCT in Germany with highly subsidized child care (N = 607), we offer treated families information and personal assistance for applications. We find substantial, equity-enhancing effects of the treatment, closing half of the large SES gap in child care enrollment. Increased enrollment for lower-SES families is likely driven by altered application knowledge and behavior. We discuss scalability of our intervention and derive policy implications for the design of universal child care programs.

In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.

Although explicit discrimination in access to social programs is typically prohibited, more subtle forms of discrimination prior to the formal application process may still exist. Unveiling this phenomenon, we provide the first causal evidence of discrimination against migrants seeking child care. We send emails from fictitious parents to > 18, 000 early child care centers across Germany, inquiring about slot availability and application procedures. Randomly varying names to signal migration background, we find that migrants receive 4.4 percentage points fewer responses. Replies to migrants contain fewer slot offers, provide less helpful content, and are less encouraging. Exploring mechanisms using three additional treatments, we show that discrimination is stronger against migrant boys. This finding suggests that anticipated higher effort required for migrants partly drives discrimination, which is also supported by additional survey and administrative data. Our results highlight that difficult-to-detect discrimination in the pre-application phase could hinder migrants’ access to universal social programs.

Understanding human behavior in its entire complexity is an ambitious if not impossible challenge. It is however possible to study particular aspects of human behavior through experiments that allow us to isolate specific facets in the decision-making process, ultimately leading to a better understanding of human behavior as a whole. This thesis covers three experimental articles on unethical economic behavior and sheds light on the motives and circumstances that lead individuals to engage in these activities. Clearly, unethical behavior in all its different manifestations can pose great risk to society – both at the large (e.g. corporate tax evasion) and small (e.g. shoplifting) scale – making it a relevant topic to be studied in economic research. Trying to understand unethical behavior through the lenses of traditional economic theory is problematic.